Many audio coding techniques exploit characteristics of human hearing. For example, a weak tone near a strong tone may not need to be coded, since the human auditory system is less sensitive for such weak tones. In traditional, so-called perceptual audio coding, quantization of different frequency data is based on models of human hearing. For example, perceptually important frequency data are allocated more bits and thus finer quantization and vice versa.
One type of audio coding is so-called transform coding. In transform coding, a block of input audio samples is transformed, e.g., via the Modified Discrete Cosine Transform, processed, and quantized. The quantization of the transformed coefficients is performed based on the perceptual importance. One audio parameter that needs to be encoded is the positions of spectral peaks. An example of spectral peak positions for an audio segment, in the transform domain, is shown in FIG. 1a. 
The spectral peak positions are typically encoded by use of a lossless coding scheme, such as Huffman coding. However, prior art solutions consume many bits on encoding of spectral peaks.